Optical coherence tomography (OCT) is a noninvasive, depth resolved, volumetric imaging technique that provides cross-sectional and three-dimensional (3D) imaging of biological tissues. OCT is commonly used to visualize retinal morphology and has become a part of the standard of care in ophthalmology [1, 2]. A limitation of conventional structural OCT, however, is that it is only sensitive to backscattered light intensity and is thus unable to directly detect blood flow or discriminate vascular tissue from its surrounding tissue. This limits the utility of structural OCT to detect blood flow abnormalities such as capillary dropout or pathologic vessel growth (neovascularization), which are the major vascular abnormalities associated with two of the leading causes of blindness, age-related macular degeneration (AMD) and proliferative diabetic retinopathy (PDR). Current standard-of-care techniques to visualize these abnormalities, fluorescein angiography (FA) or indocyanine green (ICG) angiography, require the use of an intravenous dye-based contrast agent, which can increase the risk of complications or adverse reactions and increases the amount of time required for imaging.
OCT angiography is a refinement of the OCT imaging technique that uses the motion of red blood cells against static tissue as intrinsic contrast to allow visualization of blood flow. Several OCT angiography methods have been proposed in the literature [3-7]. These OCT angiography methods enable high resolution imaging of microvascular networks and can be implemented using commercial OCT devices designed for structural OCT imaging [8, 9]. En face projection of OCT angiography-derived blood flow results onto a single plane can be used to present the clinician with angiogram images that are analogous to traditional FA and ICG angiography [10, 11].
OCT angiography has been used to quantify vessel density and flow index [12-15], choroidal neovascularization (CNV) area [16, 17], and detect retinal neovascularization (RNV) [15, 18] and macular ischemia. These analyses require accurate segmentation of the retina for proper interpretation and quantification of 3D angiograms. The simplest method of segmentation involves manual delineation of retina layer boundaries by an experienced expert, but this approach is subjective, operator intensive, and time-consuming. Automated segmentation algorithms have been proposed, but these approaches can fail in datasets exhibiting pathologies such as drusen, cystoid macular edema, subretinal fluid or pigment epithelial detachment that distort the normal tissue boundaries. Such segmentation errors necessitate manual correction approaches, but these approaches can be tedious and inefficient [19]. Thus, there remains a need for robust methods and tools to segment the retinal layers for interpretation and quantification of angiograms.